FALL 2025

AUTHORS

Lila Dodson, San Francisco University High School
Zach Sissman, Community School of Naples
Jack Whitney-Epstein, The Brunswick School

ABSTRACT

A runner’s primary lead off first base creates leverage to steal second but also exposure to pickoffs. We develop a nested sequence of logistic models to estimate (i) pickoff attempts, (ii) pickoff success given an attempt, (iii) steal attempts given no pickoff, and (iv) steal success given an attempt, using 2024 MLB data and Baseball Savant metrics. We map stage probabilities to expected runs via fixed linear weights (+0.20 for a successful steal; -0.45 for caught stealing or picked off) and optimize over lead distance to obtain a context-specific optimal lead L ∗ . Empirically, observed leads are modestly larger than optimal on average (+0.19 ft), with a larger gap on steal attempts (+0.67), consistent with unobserved intent to steal. This framework quantifies the central trade-off – greater leads increase steal success but raise pickoff risk – on a common expected-runs scale and yield actionable, interpretable recommendations within the observed support.